Evidence map›Paper›PMID 38659291›Full record

ReviewUnited European gastroenterology journal2024

Are we ready for genetic testing in metabolic dysfunction-associated steatotic liver disease?

Adele Tulone, Grazia Pennisi, Carlo Ciccioli, Giuseppe Infantino, Claudia La Mantia, Roberto Cannella, Francesco Mercurio, Salvatore Petta

Open access · hybridAbstract readReview
In one paragraph

Review in United European gastroenterology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Adele TuloneSezione di Gastroenterologia, PROMISE, University of Palermo, Palermo, Italy.
Grazia PennisiSezione di Gastroenterologia, PROMISE, University of Palermo, Palermo, Italy.
Carlo CiccioliSezione di Gastroenterologia, PROMISE, University of Palermo, Palermo, Italy.
Giuseppe InfantinoSezione di Gastroenterologia, PROMISE, University of Palermo, Palermo, Italy.
Claudia La MantiaSezione di Gastroenterologia, PROMISE, University of Palermo, Palermo, Italy.
Roberto CannellaDipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata (BIND), University of Palermo, Palermo, Italy.ORCID 0000-0002-3808-0785
Francesco MercurioSezione di Gastroenterologia, PROMISE, University of Palermo, Palermo, Italy.
Salvatore PettaSezione di Gastroenterologia, PROMISE, University of Palermo, Palermo, Italy.ORCID 0000-0002-0822-9673
University of Palermo · ARUniversity of Palermo · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD), with its steadily increasing prevalence, represents now a major problem in public health. A proper referral could benefit from tools allowing more precise risk stratification. To this end, in recent decades, several genetic variants that may help predict and refine the risk of development and progression of MASLD have been investigated. In this review, we aim to discuss the role genetics in MASLD plays in everyday clinical practice. We performed a comprehensive literature search of PubMed for relevant publications. Available evidence highlights the emergence of genetic-based noninvasive algorithms for diagnosing fatty liver, metabolic dysfunction-associated steatohepatitis, fibrosis progression and occurrence of liver-related outcomes including hepatocellular carcinoma. Nevertheless, their accuracy is not optimal and application in everyday clinical practice remains challenging. Furthermore, susceptible genetic markers have recently become subjects of great scientific interest as therapeutic targets in precision medicine. In conclusion, decisional algorithms based on genetic testing in MASLD to facilitate the clinician decisions on management and treatment are under growing investigation and could benefit from artificial intelligence methodology.

Indexed as

Genetic TestingAlgorithmsCarcinoma, HepatocellularDisease ProgressionFatty LiverGenetic MarkersGenetic Predisposition to DiseaseHumansLiver CirrhosisLiver NeoplasmsNon-alcoholic Fatty Liver DiseaseGenetic MarkersgeneticMASHMASLDmetabolic dysfunction associated steatotic liver diseasePNPLA3steatohepatitis

Identifiers

PMID38659291
PMCPMC11176907
OpenAlexW4395456412

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.